Introduction to Six Sigma

The Pareto Chart: One of the Seven Basic Quality Tools

Tuesday, April 10th, 2012


 

Pareto Charts are a specialized Histogram of count data. It arranges the Bins or Cells in largest to smallest counts and gives you an accumulation line as seen below. It is one of the Seven Basic Quality Tools.

The Pareto Chart gets its name from the use of the Pareto Principle which states “ 80% of the effect comes from 20% of the causes”. Vilfredo Pareto, an Italian economist, originated this principle by determining that 80% of the land inItalyis owned by 20% of the population. Later it was found to hold true in many things and help us focus on the critical few. With a chart like this a team can decide where to place its priority and focus ( the big hitters). This is extremely helpful when time and money is limited as it is in most cases.

How to create a Pareto (Example in Bold)

Creating a Pareto chart is slightly more difficult than a histogram but is do able to build.

  1. Decide what the problem is that you want to chart. Damaged Fruit
  2. Collect the data on the problem over a good amount of time to insure a representative sample size.
  3. Determine the classification (categories) to group the data into.
  4. Group the data by category an determine the total for each category. Bruised 100; Undersized 87; Rotten 235; Under Ripe 9; Wrong Variety 7; Wormy 3
  5. Determine the total over all the categories. 100+87+235+9+7+3= 441
  6. Calculate the percentage for each category. Bruised 22.7%; Undersized 19.7%; Rotten 53.3%; Under Ripe 2.0%; Wrong Variety 1.6; Wormy .7%
  7. Rank order the categories from the largest to the smallest. Rotten; Bruised; Undersized; Under Ripe; Wrong Variety; Wormy
  8. Calculate the cumulative percentage at each category starting from the largest and going to the smallest. Rotten 53.3%; Bruised 76.0%; Undersized 95.7%; Under Ripe 97.7%; Wrong Variety 99.3%; Wormy100.0%
  9. Construct a chart
    1. With the left scale the count starting at 0 and going to the over all total count. Scale 0 – 441
    2. With the right scale the percentage starting a 0% and going to 100%
    3. The Horizontal axis will be labeled with the categories starting on the left with the largest and going to the smallest. Many times we will add up all the categories that have only 1 item in them and label it other. But you do not want this category to be the biggest. If it is you need to group some of them in to new categories. Rotten; Bruised; Undersized; Under Ripe; Wrong Variety; Wormy
    4. Draw the bars to show the count for each category listed.
    5. Draw a line to show the cumulative percentage for each bar. Start with the left most bar (the largest) and draw it to the right.

 Well there you have a short article on how to construct a Pareto Chart. If, you have questions or comments please feel free to contact me by leaving a comment below, emailing me, calling me, or leaving a comment on my website.

Bersbach Consulting
Peter Bersbach
Six Sigma Master Black Belt
http://sixsigmatrainingconsulting.com
peter@bersbach.com
1.520.829.0090

Scatter Plots for Visualization of Relationships.

Thursday, March 15th, 2012


Scatter plots are one of the Seven Basic QC Tools. They are a graph showing you the relationship between two factors or variables. It can show you if one variable effects another. This can be a very effective tool to find out if you change one thing in a process will it affect another. To see if there is a cause and effect relationship between two factors or variables.

Creating a Scatter Plot:

To create a scatter plot you follow the below steps:

  1. First you need to collect data. This data is called paired data because it will be values from both factors gathered so you can compare one with the other. You can and should collect this paired data with other information (data) that potentially could help understand what is going on.
  2. Next you need to determine which factor you want on the horizontal axis (x) and which to put on the vertical axis (y). This is your choice, but many put the potential cause on the horizontal axis (x) and the effect on the vertical (y) axis.
  3. After you have decided which goes on which axis you need to find the minimum and maximum value of each factor. These will be used to define the each axis scale.
  4. Now we setup the Vertical (y) and Horizontal (x) axis. Both should be the same length but necessarily the same scale. These axis will make the plot (graph) look like square fit the two are the same length.
  5. Mark are each axis scale by starting with the minimum value in the lower left corner for both and the maximum value at the other end. Make sure to divide and label the rest of the axis into equal segments so you will be able to easily plot your data.
  6. Now we plot all of the x, y paired data on the graph. Do this by finding the x value on the horizontal axis and plotting a point above that value that corresponds to the y value on the vertical axis. You continue doing that until all the points are plotted.
  7. Last, but never least, label your graph with a title and a label for the vertical and horizontal axis so everyone who looks at it will be able to under stand what they see.

Interpreting a Scatter Plot:

Now that we have a scatter plot how do we interpret what we see? Is there a relationship or not? Well how we do that is look for patterns. But first, are there any outliers? These are data point that are way out side the pattern of dots that you have plotted. What these point are cause from something other than the relationship of these two variables. Note them and if you can find out what happen that created them. Now let’s look for the patterns.

  • When seeing patterns remember that the tighter together the points are clustered, the stronger the correlation (the effect) between the variables (factors) you have plotted.
  • If you find a pattern that slopes from the lower left to the upper right. This tells you that as x (horizontal axis factor/variable) increases so does the  y (vertical axis factor/variable) increases. This means there is a “Positive” correlation between the two factors/variables.
  • If you find a pattern that slopes from the upper left to the lower right. This tells you that as x (horizontal axis factor/variable) increases, the  y (vertical axis factor/variable) decreases. This means there is a “Negative” correlation between the two factors/variables.

Below is a table of pattern to help you interpret your results:

 

Correlation and Causation

Now that you see a pattern and you have found or not found a correlation between the two factors or variables, please do not assume that one caused the other to happen. That may or may not be true. You see you may very well find a correlation between the number of people using public swimming pools and the number of cooler that break down, but I do not think one caused the other. What you have to do to verify the cause is to conduct a controlled experiment and see if I hold everything else steady will the change in one make the other change as predicted in the scatter plot.

 

But even though you do not have the cause true verified you now see that something is going on and that there is a good chance that one of these factor does effect the other.

Well there you have it. All you need to know about scatter plots. Or at least the basics on how to construct and interpret them. If, you have questions or comments please feel free to contact me by leaving a comment below, emailing me, calling me, or leaving a comment on my website.

Bersbach Consulting
Peter Bersbach
Six Sigma Master Black Belt
http://sixsigmatrainingconsulting.com
peter@bersbach.com
1.520.829.0090

How to build a good Check Sheet

Monday, March 5th, 2012


In an earlier article “The Check Sheet – Simple but Powerful” I talked about the power of a check sheet and how some use them. Here in this article I’m going to talk you through constructing one. Check Sheets are one of the Seven Basic QC Tools. You will usually construct a check sheet for one of two reasons. To collect information (data) about something, or to help you remember to do some thing. Lets look at both.

Collecting information:

When collecting information, check sheets are lists of items and the frequency that the item occurs. They can be made in so many different ways that many times, we don’t think of them as a list, but they are.

Simple Table Check Sheet

Figure 1: Simple Table Check Sheet

The most recognized check sheet is the simple table (Fig. 1). Here you create two columns, the first will be your categories and the second one the count or frequency it is detected. In the categories column you list in each row either an attribute or a range of values that you what to know information about. In figure 1 we have ranges of inches so we can capture different sizes (categories) of some measurement we are making.

The right column you label as frequency and put “tick” marks ever time you find a value in that measurement range. If you arrange the tick marks as seen in figure 1 (groups of five) you will see that your check sheet will look like a Histogram and you now can see the shape of the measurement distribution . With this information you can see the highest , lowest, middle and most frequent value that you collected easily from this table.

The same can be done for attributes or characteristics. For instance you could want to collect the number of each color car that drives by your house. In this case the left column would be a list of colors while the right tick marks for each car color that passed.

The Picture Check Sheet

Figure 2: Picture Check Sheet

Another very handy check sheet is what I call a “Picture” check sheet (Fig. 2). Here you take a picture of something you want to collect information about and you mark on the picture where something occurred. In figure 2 you can see a picture of a shoe. On it are red x’s  where defects were seen during an inspection. This type of check sheet is great for showing you were something occurred most frequently. Here on this shoe most of the defects are in the toe. So now we can work toe issue as it is the most frequent type of defect on this shoe. This could have been done in a table but find the correct description of where the defects are is sometimes difficult so using a picture make’s it real easy to see.

 

 

Help to Remember:

Figure3: Grocery List

Another type of check sheet we use a lot is a “Help me remember” sheet. It help us to remember what to do especially when we have complex task to perform and we do not what to forget anything. The one I know many use is a shopping list. Here you just have a list of thing to get and as you get them you check or cross them off the list. Assembly instructions can be a check sheet. Also think about restaurants where the waitress take your order she creates a check sheet to insure your order is completed as you ordered.

Another good check sheet at a restaurant that I frequent take the waitress out of the loop by making the menu a check sheet. Here you pickup the menu items you want by marking what you want to eat right on the menu it self. Then you hand it to the cashier they ring you up and the menu goes to the kitchen to be filled. Great idea, one less opportunity for a mistake on my order.

 

In  Summary

As you can see all of these answer such questions as:

  • Has all the work been done?
  • Has all the inspection been done?
  • How frequently a problem occurs?
  • What should I do next?
  • Have I done everything?

Well there you have how to build a good check sheet. If, you have questions or comments please feel free to contact me by leaving a comment below, emailing me, calling me, or leaving a comment on my website.

Bersbach Consulting
Peter Bersbach
Six Sigma Master Black Belt
http://sixsigmatrainingconsulting.com
peter@bersbach.com
1.520.829.0090

How to build a good Histogram

Monday, February 20th, 2012


A histogram, one of the Seven Basic QC Tools,  is a very good tool to use to picture what a set of data looks like.  It give shape to a set of data by grouping the data into “cells.” It shows you the spread or dispersion and the central tendency which can be used to compare to a standard or another group of data. In this way it can be an excellent troubleshooting tool by using it to compare different suppliers, equipment, processes to reveal their differences or similarities.

Although most statistical or spreadsheet software can create a histogram for you very easily I am going to talk you through how to create a good histogram by hand. The real key to a good histogram is to get the correct number of “cells” for the size of the set of data you have. If you have to few or to many it will not give you much of a feel for the spread or center of the data you have. Too few looks like a big clump and too many looks like a broad scatter of points. Neither shows or tells you much about your data. So here is what you do to build a histogram by hand.

  1. Find your largest and smallest number in the data and calculate the data range by subtracting the smallest value from the largest one.
  2. Now we determine the all important number of cells for our histogram. These cells will be the columns you see in a histogram. The “Six Sigma Handbook” by Thomas Pyzdek shows two ways to get the correct number of cells for you data. This first number will change a bit as you do some calculations but they are a very good starting point. The first is to use the table below.

Sample Size

Number of Cells

100 or less

7 to 10

101-200

11 to 15

201 or more

13 to 20

 

The second method, using a calculator, you can take the square root of the sample size and round that number to the nearest integer.

  1. Next we determine the width of each cell by dividing the range that you found in the step 1 by the number of cells we determined in step 2.

 

Once you have calculated the cell width round it to a convenient number. Doing this will affect the number of cells in your histogram, but that will be ok.

  1. Next we will computer the “cell boundaries.” Look at a cell as a range of values of your data. The cell boundaries define the start and end point for each cell in your histogram. Since it will be these start and end point we will make them one more decimal place more than our data values.  Thus if our data values are integers (1, 12, 36)  then our cell boundaries will be one decimal place (xx.x).
  2. Now we determine the low boundary of the first cell. This boundary has to be set less than the smallest value of your data set.
  3. Now that the lowest cell boundary is determined all the other cell boundaries are determined by adding the cell width to the previous boundary. Continue this until the upper boundary  is larger than the largest value in the data set.
  4. Now go through the data that you have and determine in what cell each value goes and make a tick mark in that cell (bounded by the boundaries you calculated).
  5. Count the ticks in each cell and record the total count in each cell.
  6. Now we have all the statistics to create the histogram. First, on graph paper, draw a horizontal line near the bottom of the page. Leave room below to label the cell boundaries on this line.
  7. Starting with the lowest cell boundary, equally space all the boundaries along this line.
  8. Next at the left end of the horizontal line draw a vertical line. This lines length will be just longer than the largest cell count that you found. This line should be label from 0 to the largest cell count or just beyond. This is the count or frequency axis
  9. Last you draw in the columns (or bars) for each of the cells up to the count/frequency of that cell .

 

So below is a histogram made in Minitab but let me give you the basic information about its data.

    • Lowest Value = 596.2
    • Highest Value = 604.2
    • Range = 8.0
    • There are 200 values in this set of data

 

 

Now, let’s see how close it is to the manual method.

  1. Number of cells: Table value 15; Square root method 14.142
  2. Cell Width: Table: 8/15 = .5333 ~  .5 Square Root: 8/14.142 = .5656 ~ .5
  3. lowest Cell Boundary < 596.2  (and one decimal more) = 596.15
  4. All the other boundaries (Largest must be larger than the highest value [604.2]

 

# Cells Lower Boundary Cell Center Upper Boundary Lowest Val=

596.2

1

596.15

596.4

596.65

Cell Width=

0.5

2

596.65

596.9

597.15

 

3

597.15

597.4

597.65

 

4

597.65

597.9

598.15

 

5

598.15

598.4

598.65

 

6

598.65

598.9

599.15

 

7

599.15

599.4

599.65

 

8

599.65

599.9

600.15

 

9

600.15

600.4

600.65

 

10

600.65

600.9

601.15

 

11

601.15

601.4

601.65

 

12

601.65

601.9

602.15

 

13

602.15

602.4

602.65

 

14

602.65

602.9

603.15

 

15

603.15

603.4

603.65

 

16

603.65

603.9

604.15

 

17

604.15

604.4

604.65

Highest Val=

604.2

 

Well there you have how to build a histogram by hand. . If, you have questions or comments please feel free to contact me by leaving a comment below, emailing me, calling me, or leaving a comment on my website.

Bersbach Consulting
Peter Bersbach
Six Sigma Master Black Belt
http://sixsigmatrainingconsulting.com
peter@bersbach.com
1.520.829.0090

 

The Cause and Effect Diagram

Friday, February 10th, 2012

The Cause and Effect Diagram (C&E Diagram), sometimes called the Fishbone or Isjikawa Diagram, is one of the Seven Basic QC Tools. It is a simple but effective way to organize a group or persons knowledge about the potential causes of a problem or issue and display the information graphically. You might want to use this if you want to stimulate the thinking of a group around an issue. Or  so you can see the relationships between different potential cause of an issue or problem.

It was originally created and used by Dr. Kaoru Ishikawa and is sometimes called an Ishikawa Diagram. Also, because of its shape it is called a Fishbone Diagram.

There are several easy steps to constructing a good C&E Diagram, they are as follows:

  1. Develop a team of people that are involved in the process area where this issue or problem occurs. Never try to do this alone because as a team each member brings to the discussion a different perspective of the issue or problem at hand.
  2. Have the team Brainstorm  to find all possible causes of the problem. Remember Brainstorming is a process of collecting ideas. You what as many as you can get even if some seem strange.
  3. Now have the team, using Affinity Diagramming, organize the results into rational categories and sub-categories. Many times getting these categories started or named is difficult so some start with a few basic one at the get go. The four “M’s” are commonly used ( Manpower, Machine, Material, Method) but other are just as good such as environment. As you can see by the diagram above they used Assembly, Process, Fabrication, Design. To them those worked.
  4. Now we start constructing the diagram by Drawing a box on the far right hand side of  a large sheet of paper and draw a horizontal arrow that points to the box.
  5. Inside the box, write the description of the problem or issue you are trying to solve.
  6. Next write the names of the categories above and below the horizontal line. Think of these as branches from the main trunk of a tree.
  7. Then draw arrows from those categories to the trunk ( the horizontal arrow drawn earlier).
  8. After that write in the next level of Sub-Categories and draw in arrow to their main categories. Think of these as limbs and twigs on the branches.
  9. Continue repeating step 8 until all of the sub-categories have been entered.

If you complete this exercise and find a lack of lower level branches and twigs this would suggest the team has a superficial understanding of the problem. In which case you will have to use GIMBA and gather more information.

Once you have this information you need to verify the information you have. Verify by going and collecting data to confirm which of these “potential” causes really do contribute to the issue. Your Diagram may be very large and doing this verification would take to long to do all, so for an alternative the team should prioritize the categories and look at the top few.

Once you have the big hitter then you can start trying to figure out why these occur and a good tool to start with is the 5 Whys.

Well there you have a short article on how to construct and interpret a Cause and Effect Diagrams. Stay in touch as I explain how to construct and interpret Histograms. If, you have questions or comments please feel free to contact me by leaving a comment below, emailing me, calling me, or leaving a comment on my website.

Bersbach Consulting
Peter Bersbach
Six Sigma Master Black Belt
http://sixsigmatrainingconsulting.com
peter@bersbach.com
1.520.829.0090

The Seven Basic Quality Control Tools

Saturday, November 26th, 2011

Product or service quality is everyone’s responsibility, from a “Mom and Pop Shop” to an international corporation. So I thought I give those who don’t know how to look at the quality of what they do, a set of basic tools. Quality professional have all heard of “The Seven Basic Quality Control Tools” so here they are.

The Seven Basic QC (Quality Control) Tools are a given set of graphical techniques identified as being helpful in troubleshooting issues related to quality[1]. These seven are called basic because they can be used easily by anyone to solve the vast majority of quality-related issues. Many quality professional believe these were originated by Dr. Ishikawa, a world renowned quality professional.  But, he would tell you that he was inspired by the “Seven Famous Weapons of Benkei[2] . The designation as the “Seven Basic Tools of Quality” arose in postwarJapan.

The Tools

  1. 1.      Cause and Effect Diagrams: (Fishbone Diagrams, Ishikawa Diagrams)

These diagrams are tools that organize a group or persons knowledge about the causes of a problem or issue and display the information graphically.

 

It was originally created and used by Dr. Kaoru Ishikawa and is sometimes called an Ishikawa Diagram. Also, because of its shape it is called a Fishbone Diagram. In general what you do is brainstorm ideas (causes) then group them in to categories. Those categories become the many branches of the Cause and Effect diagram.

  1. 2.      Check Sheets:

This is another simple but powerful tool. Check Sheets are lists of items and the frequency that the item occurs. They can be made in so many different ways that many times, we don’t think of them as a list, but they are. below are two, one that kind of looks like a list the other not so much. On the shoe the defects are marked with an “x” in the location it was found.

They are use to answer many important questions such as:

  • Has all the work been done?
  • Has all the inspection been done?
  • How frequently a problem occurs?

They are often used to remind individuals doing complex tasks of what to do and in what order. They are also used many times in conjunction with other tools to help quantify or validate information.

  1. 3.      Control Charts:

Control charts are the most difficult of the seven tools to use. They are seldom the method  of choice. When a process step is important, we would prefer that the step not vary at all. ONLY when this can not be accomplished in an economical way does one choose to use a control chart. Below is an “XBar-R Chart” also called an “Average and Range Control Chart”.

Control charts are only useful if the step (operation or function), over time, exhibits measurable random variation. Control charts display the data over time (Time is on the x axis above listed as sample). Control Limits (the red lines) are displayed on control charts, where data falling within the control limits are considered “normal” variation. Any point outside the control limits are considered “special caused” variation and need to be look at and corrected through an action plan. If you create a control chart, you must also have with it an action plan.

Besides control limits for control charts, there are several other type of trends (runs) that can indicate an out-of-control process.

What I have shown above is only one type a control chart and one of the simplest to use but there are several others (not so simple to use). Below is a Decision Tree Diagram of the different type and there use. Be sure you understand the application of each control chart or get help if you plan to use one of these.

  1. 4.      Histograms:

Histograms are a “picture” of a set of data (or information). It is created by grouping the data you collect in to “Cells” or “Bins” (Bars in the chart below).

Histograms take your data and give it a shape (Distribution). With this, you can see the data sets spread, central tendencies, and if it meets requirements. As you can see, it is a valuable troubleshooting tool. You can take it a compare differences between machines, people, suppliers etc. Never use a histogram alone always also plot it in a time ordered  plot (run chart).

  1. 5.      Pareto Charts:

Pareto Charts are a specialized Histogram of count data. It arranges the Bins or Cells in largest to smallest counts and gives you an accumulation line as seen below.

The Pareto Chart gets its name from the use of the Pareto Principle which states “ 80% of the effect comes from 20% of the causes”. Vilfredo Pareto, an Italian economist, originated this principle by determining that 80% of the land inItalyis owned by 20% of the population. Later it was found to hold true in many things and help us focus on the critical few. With a chart like this a team can decide where to place its priority and focus ( the big hitters). This is extremely helpful when time and money is limited as it is in most cases.

  1. 6.      Scatter Diagrams:

Scatter plot are a very simple tool to use to see if there is a correlation between two things (i.e. does one thing lead to another). I always before going into any major analysis of data, plot the data in some way to get a “gut feel” of what is happening. This tool lets you create a simple picture showing how two or more variables change “together”.

As one can see in the chart above the fruit on the tree increase in weight the longer it is on the tree. In scatter charts we see if one thing relates (correlates) with another. Below is a set of chart that shows some of the relationships you might find with this tool.

  1. 7.      Stratification: (Flow Charts, Run Charts, etc.)

To me Stratification is a catch-all for summarizing, picturing, or applying some tool to data so you can understand what is happening. Stratification is the process of dividing members of a population into homogeneous subgroups before using it. The data (strata) should be mutually exclusive: every element in the population must be assigned to only one subgroup (stratum). The data should also be collectively exhaustive: no population element (data) can be excluded.

That’s a mouthful, but if you look at above six tools all of them do this stratification of the data. In many texts they list either flow charts or run charts under this seventh tool area. A run chart is just the “Individuals Chart” of the above control chart without control limits. A flow chart takes a group of steps in a process and summaries them into a map of the way the process works. They are sometimes called a Process Map or a Process Flow Map.

They are created to:

  • Create a common understanding of the process flow
  • Clarify steps in a process
  • Uncover problems and misunderstanding in a process
  • Reveal how a process operates (good and bad)
  • Helps you ID places for improvement.

Well there you have a short description of the Seven Basic Quality Tools. Stay in touch as I go into each tool with details of how to construct and interpret them. If, you have questions or comments please feel free to contact me by leaving a comment below, emailing me, calling me, or leaving a comment on my website.

Bersbach Consulting
Peter Bersbach
Six Sigma Master Black Belt
http://sixsigmatrainingconsulting.com
peter@bersbach.com
1.520.829.0090



[2] Ishikawa, Kaoru (1990), Introduction to Quality Control (1 ed.), Tokyo: 3A Corp, p. 98, ISBN 9784906224616, OCLC 23372992

 

Do Customers Know What They Really Want?

Friday, August 19th, 2011


In an earlier article, “Creating Customer Value” I explained that to insure you are creating customer value at any given step or your process you need to ask and answer three questions with a yes. They are:

  1. Did the thing in the process change?
  2. Does the customer care about this change?
  3. Was it done right the first time?

 

Well in those three questions is one from the customers view point “Does the customer care about this change?” Many time we do not really understand or see this view (customer cares about) clearly. We might say for them to care about something that they must understand what they want. Well let try to set the record straight on this one.

First, I’d like to start with my definition of customer value (Want):

Customer value is a product or service that is received by the customer at the right time, place, cost and functions AS DEFINED BY THE CUSTOMER.

It should be noted that time, place, and cost are all parts of your delivery process (which we try to streamline) and only function addresses the actual product or service once in hand of the customer. In the titles question many times we only focus on this “function” but if we do, we miss three other major parts of customer value and may loose the customer because of that narrow sightedness. So DO NOT FORGET the other parts of customer value.

But now let’s talk about this “function” in terms of what the customer wants. I have had discussions with some colleagues that will hold fast; that the customer DOES NOT always know what they want. And, I can not fault them on it when it come to the exact details of what they want. A good example I was given was my colleague said his wife’s birthday was coming up and he had no idea what to get her. I believe him, I have the same problem but some how he and I both get something they like. How does that happen? I think it because we don’t know the details but we do have some more global thoughts (even if they are what NOT to get her). So in reality we do have some, even though vague, ideas of what to get. And those thought will lead us to some places that we think we can find that present.

For instance, my wife love to help in the remodeling of the house, but it will not be “my friendly hardware store” that I will go to purchase her present. No I’ll, and so might my colleagues, go to stores that my wife goes and buys things for herself. I do know what she likes and dislikes as I see what she purchases at these stores. Plus with a little help, I hope, for the store personnel, I can find something that will be just the right thing. It usually works well.

So does the customer always know what they want? I say a BIG YES!! Maybe not the details. But if I walk into your place a business there was a reason and your sales persons will need to understand that and work with me to fill in the details or I will probably go somewhere where I will find the help.

Well there you have it. Customers do know what they want even if it is some what vague. So I hope you are listening when they show up. There are other articles on Customer value that you can find on my blog http://www.sixsigmatrainingconsulting.com/knowledgebase/ . As always, if you have any questions feel free to contact me.

 

Bersbach Consulting

Peter Bersbach

Six Sigma Master Black Belt

http://sixsigmatrainingconsulting.com

peter@bersbach.com

1.520.829.0090

 

The NFL Talks Missing some Six Sigma Rigor

Tuesday, March 22nd, 2011

As practitioners of Six Sigma you may have caught this, but there are two important elements that the NFL talks have missed and I feel will lead to poor results or none at all. These two important elements are the concept of Customer Value, or some may say “stakeholder” value. Second is the concept of Teamwork. This second one I would think they would get since Football is a “Team” sport, but maybe not.

Customer Value

Customer or stakeholder value in solving an issue, involves insuring we know who the customer and or stakeholders are. Generally speaking you look at where the money comes from, Customers (fans). This is why in most places we talk about “customer Value”.  I also like to expand the “customer” to be all stakeholders because without all stakeholders a product can not be produced. So I define Stakeholders in three. Those three groups are:

1.      Customers – Where the income (money) comes from.

2.      Stockholders/owners – Who’s money is invested in the business and that investment is being spent to produce the value for the customer. Yon this group because in all businesses you have to spend money to make money. Customer pay for product at delivery usually not in advance.

3.      Employees/ Players – Who perform the “manufacturing of the product” for the customer.

Both the Stockholders/owners and the Employees/Players are investing time and/or money to create a value for the customer.

So where is the customer in the NFL talks? In businesses that are working similar issue this point and input would not be left out of the discussion, where clearly it is in the NFL talks. Businesses include them because the solution may not even be focus on increasing value to the customer. Which means insuring that the change will increase customer value thus increase profits.  When customers are left out ( even though they don’t know they are) they go some where else. That is what happened in the steel industry. That is also what was and may still be going on in the automotive industry.

By the way value can be defined (Seen) by asking three simple questions and you are creating value IF and only IF you answer yes to all three. They are:

  1. Does the Customer Care ( Is the customer willing to pay for this change)
  2. Did the “thing” in the process change. (With all the step you go through in changing a Flat tire ONLY removing the flat and placing the new tire on the car are value added.
  3. Last was it done right the first time. Customer do not like to pay for repairs or rework.

Teamwork

Teamwork in not decision making by concession or compromise it is decision making by consensus  or accord. To do that you have to pick you team members carefully. They need to come from all three stakeholder groups and each member needs to have the following qualities:

  1. Wants to make a difference by improving the process creating a better working place.
  2. Is willing to work on and support the team “project”.
  3. Is willing to take the risk of offering “Wild” ideas
  4. Is willing to withhold judgment
  5. Can “Piggyback off other’s ideas
  6. Is willing to LISTEN (no side talks)

 

As you can see not all the stakeholders are on the team and those that are there do not or will not agree with the 6 items above. In the six sigma world of problem solving this will only lead to disaster.

Well there you have my thoughts on the NFL talks. If, you have questions or comments please feel free to contact me by leaving a comment below, emailing me, calling me, or leaving a comment on my website.

 

Bersbach Consulting
Peter Bersbach
Six Sigma Master Black Belt
http://sixsigmatrainingconsulting.com
peter@bersbach.com
1.520.829.0090

Measurement

Wednesday, October 20th, 2010

[Note: Most of the information for this article comes from “The Six Sigma Handbook”[i]]

Why do we measure things? To see how things are, or if change has occurred or to understand something. Measurement is just looking at something and describing it in numbers. The rules (mapping functions) that we use to describe the “thing” in numbers provide us with a model of reality. If this model is correct (valid) we can learn about the real world by studying the model and the numbers that it predicts. Without these measurements systems astronomers could not describe the make up of galaxies billions of light-years away from us.

Every questions we have starts in the real world But to understand the question and come up with the answer we use mapping functions (rules) to describe the real world question using numbers. There are times when we map to a non numeric entities in the real world, Like a question about color but we convert these into numbers like the number of red things in a room. These characteristics (elements) are X’s. The “numbers” are Y’s derived using the mapping function as a transfer function of the elements into numbers.

A good example of this we all can relate to is the fuel tank on your car. It would be nice to know how much fuel is in your tank? That would be a measurement of the amount of fuel in your tank.


Real World – Your fuel tank with some amount of fuel in it.

Mapping function – A float with a sensor on a spindle connected to a fuel gage. The gage marked off in numerical intervals. Plus YOU reading the indicator.

Numbers – The gage needle pointing to a numerical value on the gage (like the 1/8 mark just above Empty (0))

Usage – Time to get gas!


Measurement Scales

Not all data (numbers we collect) are created equal. That does not mean some are better than others is just means that some tells us more information than other. You will find that our numbers fall into one of four scales. In teams that I have worked with I always bring up the discussion of measurement scales because not everyone looks at how they would measure something in the same way. Some may look at the fuel tank about as fuel empty or half full, other may talk in gallons of fuel. With that said we need to understand the scale we are going to measure the real world in. The scale of the data to be collected in the measurement process. .So here are the four measurement scales.

  • Nominal Scale – These are numbers that only indicate the presents or absence of an attribute. All we can do here is count items with or without this attribute.
  • Ordinal Scale – This scale gives us a little bit more information. With this scale we can say if an item has more or less of an attribute With this scale we can rank order items.

  • Interval Scale – This scale is use when we are measure the differences between observations. Interval scale numbers that are equally different represent differences of equal magnitude. The zero vale of an interval scale is arbitrary.

  • Ratio Scales – This scale is like Interval Scale except it has a true zero point. In other words you can have nothing less than zero.

Well there you have my thoughts on Measurement and the importance of your scale of measurement. Next time I am going to discuss the different statistical tool use for different scales of measurement. If, you have questions or comments please feel free to contact me by leaving a comment below, emailing me, calling me, or leaving a comment on my website.


Bersbach Consulting
Peter Bersbach
Six Sigma Master Black Belt
http://sixsigmatrainingconsulting.com
peter@bersbach.com
1.520.829.0090




[i] Thomas Pyzdek The Six Sigma Handbook, 2003, McGraw Hill

Understanding Variation

Tuesday, September 28th, 2010

There is variation everywhere. Look around there are no true clones of anything, everything is at least slightly different. Even in identical twins there is a difference that the parents can see to tell them apart. It is variation in the world that feeds evolution. It is this variation that allows life to survive on this planet. Not everything survives but those that adapt (change/ vary) to the changing world do survive. So as a society we tend to classify things at any given moment. Classification gives us an ability to take a look at things and figure out what makes them “tick” (survive). These classification come in one of two types. Those two types are categorical (discrete) information (data) and numerical (continuous) information (data).

Variation Classifications:

Let’s take a good look at these two types of classification of information I call data. First there is Categorical (discrete) data.

Definitions:

Categorical – Belonging to a category.

Categorize – To describe by labeling or giving a name to a group of characteristics.

Discrete – Apart or detached from others; separate; distinct.

Categorical data can only be one of a limited number of non-numerical choices. It is sometimes, in numerical terms (becomes numerical data), called count data because the only way to measure it is by counting. Examples of this type of data are:

  • Best/better/worse
  • Small/Medium/Large
  • Restaurant $$ ratings
  • Movie ** ratings
  • Pass/Fail
  • Yes/No
  • Red cars
  • Doctors
  • Broken
  • Repaired

Second is Numerical ( continuous) data.

Definitions:

Numerical – of  or pertaining to numbers; of the nature of a number.

Continuous – uninterrupted in time; without cessation.

Numerical data is from a measuring process. Examples of this type of data are:

  • Height
  • Weight
  • Length
  • Depth
  • Voltage
  • Time

Business and Variation:

In businesses we compensate for variation to try to meet customer needs and expectations. This compensation cost money. In Six Sigma we try to understand and deal with this variation. We use statistics to help recognize and thus assess the variation by organizing it in a meaningful way. Statistics help change assumptions to conclusions about where the errors (variations) are and how bad it is affecting our business. Statistics help “Picture” the variation we feel or think is happening.

Definition

Statistic – a numerical fact usually computed from a sample.

Well there you have my thoughts on understanding variation. Next time I am going to discuss measuring that variation and the proper scale of measurement to use depending on the type of  variation you are trying to measure. If, you have questions or comments please feel free to contact me by leaving a comment below, emailing me, calling me, or leaving a comment on my website.


Bersbach Consulting
Peter Bersbach
Six Sigma Master Black Belt
http://sixsigmatrainingconsulting.com
peter@bersbach.com
1.520.829.0090